Browsing by Author "Britten, Nicholas"

Now showing 1 - 5 of 5
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    Characterizing Level 2 Automation in a Naturalistic Driving Fleet
    Perez, Miguel A.; Terranova, Paolo; Metrey, Mariette; Bragg, Haden; Britten, Nicholas (Safe-D University Transportation Center, 2024-01)
    Introducing automation into the vehicle fleet disrupts how vehicles operate and potentially affects what drivers do with these features and expect from vehicle performance. Therefore, it is imperative to study driver adaptations in response to these innovations. This investigation leveraged 47 vehicles from the Virginia Tech Transportation Institute Level 2 (L2) Naturalistic Driving Study to analyze driver behavior with L2 automation features. Results showed no sizeable differences between periods of L2 feature usage and general driving periods with respect to time-of-day and calendar-related metrics. Most L2 feature usage occurred on motorways, following design expectations. L2 features were activated for 7.2 minutes in trips lasting an average of 22.8 minutes, or about 32% of the L2 trip duration. Driver-initiated overrides were predominantly done by braking or accelerating the vehicle, with steering-based overrides being minimal and likely involving lane changes without using a turn signal. Intervention requests were the most common takeover request, followed by requests due to insufficient driver hand contact with the steering wheel. Findings suggest that as L2 features penetrate the U.S. fleet in non-luxury consumer vehicles, system usage will be common and comparable with previous findings for luxury offerings. While evidence of potential system misuse was observed, future work may further operationalize system misuse and assess the prevalence of such behaviors.
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    Coming In! : Communicating Lane Change Intent in Autonomous Vehicles
    Lee, Seonghee; Britten, Nicholas; Block, Avram; Pandya, Aryaman; Jung, Malte; Schmitt, Paul (ACM, 2023-03-13)
    Lane changes of autonomous vehicles (AV) should not only succeed in making the maneuver but also provide a positive interaction experience for other drivers. As lane changes involve complex interactions, identification of a set of behaviors for autonomous vehicle lane change communication can be difficult to define. This study investigates different movements communicating AV lane change intent in order to identify which effectively communicates and positively affects other drivers’ decisions. We utilized a virtual reality environment wherein 14 participants were each placed in the driver’s seat of a car and experienced four different AV lane change signals. Our findings suggest that expressive lane change behaviors such as lateral movement have high levels of legibility at the cost of high perceived aggressiveness. We propose further investigation into how balancing key parameters of lateral movement can balance in legibility and aggressiveness that provide the best AV interaction experience for human drivers.
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    Human Factors of Driving Automation: Evasive Maneuver Event Response Evaluation
    Britten, Nicholas; Hankey, Jonathan M. (Safe-D National UTC, 2023-03)
    An increasing number of conditionally automated driving (CAD) systems are being developed by major automotive manufacturers. In a CAD system, the automated system is in control of the vehicle within its operational design domain. Therefore, in CAD the vehicle is capable of tactical control of the vehicle and can maneuver evasively by braking or steering to avoid objects. During these evasive maneuvers, the driver may attempt to take back control of the vehicle by intervening. A driver interrupting a CAD vehicle while properly performing an evasive maneuver presents a potential safety risk. To investigate this issue, 36 participants were recruited to participate in a Wizard-of-Oz research study. The participants experienced one of two evasive maneuvers on a test track. The evasive maneuver required the CAD system to brake or steer to avoid a box placed in the lane of travel of the test vehicle. Drivers glanced toward the obstacle but did not intervene or prepare to intervene in response to the evasive maneuver. Importantly, the drivers who chose to intervene did so safely. These findings suggest that after experiencing a CAD vehicle for a brief period, most participants trusted the system enough to not intervene during a system-initiated evasive maneuver.
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    An On-Road Assessment of Driver Secondary Task Engagement and Performance during Assisted & Automated Driving
    Britten, Nicholas (Virginia Tech, 2021-12-15)
    Increasingly, many of today’s vehicles offer Society of Automotive Engineers (SAE) partially automated driving (PAD) and a limited number of SAE conditionally automated vehicles (CAD) are being developed. Vehicles with PAD systems support the driver through longitudinal and lateral control inputs. However, during PAD the driver must be prepared to take control of the vehicle at any time, requiring them to monitor the environment and PAD system. In contrast, during CAD the driver is not required to monitor the environment or system but must take control when prompted by the system. Given the ability of CAD vehicles to operate in PAD and manual driving, it is important to consider drivers’ mode awareness, that is, their ability to follow the state of the automated system and predict the implications of this status for vehicle control and monitoring responsibilities. In addition, since CAD does not require drivers to keep their visual or attentional resources on the driving task or environment, drivers are allowed to perform secondary tasks (i.e., non-driving related tasks (NDRTs)). Given that drivers will freely choose what types of tasks they do during CAD it is important to build an understanding of whether drivers will choose to engage in NDRTs in the CAD state, and drivers’ ability to perform NDRTs during CAD. To investigate driver’s mode awareness after transitions between modes, their willingness to engage in NDRTs, and their ability to perform scheduled smartphone NDRTs, an on-road experiment was conducted using the Wizard-of-Oz (WoZ) method to simulate a vehicle capable of Assisted Driving (similar to PAD) and Automated Driving (similar to CAD). A total of 36 drivers completed the on-road experiment, and experienced stable periods of manual driving, Assisted driving, and Automated driving, as well as transitions between these modes. After each transition, participants’ mode awareness was measured. Drivers’ performance of NDRTs and behavioral adaptation during Automated Driving was assessed by asking them to complete scheduled tasks on their smartphones. To measure driver willingness to engage in unscripted NDRTs during automated driving, participants were allowed to freely choose to engage in smartphone NDRTs between the scheduled tasks. It was hypothesized that drivers’ mode awareness of Assisted and Automated Driving and their willingness to engage and perform NDRTs during Automated Driving would increase with system exposure over the five planned activation periods of Automated Driving. Results from a mixed-model ANOVA showed that participants’ mode awareness of their role in Automated Driving statistically significantly increased from the first activation to the subsequent activations. There was no statistically significant effect of activation period on drivers’ willingness to engage in NDRTs, as measured by the mean percentage of time drivers chose to engage in NDRTs during Automated Driving, or driver’s ability to perform tasks, as measured by the mean task completion time of the experimenter administered smartphone NDRTs. The mean number of participants who chose to engage in an NDRT (73.8%) and the percentage of time spent on NDRTs per Automated Driving activation period (M=20.37%; SD=23.9), indicated that drivers were willing to engage in NDRTs during Automated Driving. In addition, drivers showed a high level of task performance, completing 95% of the scheduled NDRTs correctly. Altogether, these results suggest that drivers are willing to engage in and perform NDRTs during Automated Driving and that driver behavior during Automated Driving is consistent and stable during a two-hour exposure period. Finally, the findings indicate that requiring the participant to control the vehicle manually for a brief period prior to transitioning to a level of automation that allows the driver to take their visual and attentional resources away from the roadway environment results in statistically significantly less NDRT engagement compared to when participants transition directly to this level of automation. Overall, the findings from this study have methodological and potential system design implications that can help guide the future research on and design of automated driving systems.
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    Safe to Approach: Insights on Autonomous Vehicle Interaction Protocols with First Responders
    Lee, Seong Hee; Patil, Vaidehi; Britten, Nicholas; Block, Avarm; Pandya, Aryaman; Jung, Malte; Schmitt, Paul (ACM, 2023-03-13)
    As autonomous vehicles (AV) become increasingly common on our roads, it is important for first responders - police officers, firefighters, and emergency medical services to learn new interaction protocols as they can no longer rely on those applied to human-driven vehicles. This study identifies critical pain points and concerns of first responders interacting with AVs on the road. We explore 7 different designs that communicate that an AV is in park and is safe to approach and analyze how first responders perceive these designs in terms of clarity and safety. We conducted qualitative interviews with 9 first responders and gained insights on how the needs of first responders can be integrated within the AV design process. As a result, we identify an AV safe park state communication protocol that would be ideal for first responders. Additionally, we derive a guideline for effective communication methods that can be used in the design of these vehicles establishing research methods that involve emergency responders within the loop.